UMLS::Similarity
SYNOPSIS
This package consists of Perl modules along with supporting Perl
programs that implement the semantic similarity and relatedness
measures described by Leacock & Chodorow (1998), Wu & Palmer (1994),
Nguyen and Al-Mubaid (2006), Rada, et. al. 1989, Patwardhan (2003),
Jiang & Conrath (1997), Resnik (1995), Lin (1998) and a simple path
based measure.
UMLS::Similarity requires the UMLS::Interface module to access
the Unified Medical Language System (UMLS) in order to determine
the similarity between two UMLS concepts.
The Perl modules are designed as objects with methods that take as
input two concepts from the UMLS. The semantic relatedness of these
concepts is returned by these methods. A quantitative measure of
the degree to which the two concepts are related has wide ranging
applications in numerous areas, such as word sense disambiguation,
information retrieval, etc. For example, in order to determine which
sense of a given word is being used in a particular context, the sense
having the highest relatedness with its context word senses is most
likely to be the sense being used. Similarly, in information retrieval,
retrieving documents containing highly related concepts are more likely
to have higher precision and recall values.
The following sections describe the organization of this software
package and how to use it. A few typical examples are given to help
clearly understand the usage of the modules and the supporting
utilities.
SEMANTIC RELATEDNESS
We observe that humans find it extremely easy to say if two words are
related and if one word is more related to a given word than another.
For example, if we come across two words -- 'car' and 'bicycle', we know
they are related as both are means of transport. Also, we easily observe
that 'bicycle' is more related to 'car' than 'fork' is. But is there
some way to assign a quantitative value to this relatedness? Some ideas
have been put forth by researchers to quantify the concept of
relatedness of words, with encouraging results.
A number of different measures of relatedness have been implemented in
this software package. These include a simple edge counting
approach. The measures require the UMLS-Interface that define UMLS
concepts, and some basic relationships between these concepts.
CONTENTS
All the modules that will be installed in the Perl system directory are
present in the '/lib' directory tree of the package. These include the
semantic relatedness modules --
UMLS/Similarity/lch.pm
UMLS/Similarity/path.pm
UMLS/Similarity/wup.pm
UMLS/Similarity/nam.pm
UMLS/Similarity/cdist.pm
UMLS/Similarity/res.pm
UMLS/Similarity/lin.pm
UMLS/Similarity/jnc.pm
UMLS/Similarity/random.pm
UMLS/Similarity/vector.pm (beta)
-- present in the lib/ subdirectory. All these modules, once installed
in the Perl system directory, can be directly used by Perl programs.
The package contains a utils/ directory that contain Perl utility
programs. These utilities use the modules or provide some supporting
functionality.
umls-similarity.pl -- returns the semantic similarity of two
terms or UMLS CUIs given a specified
measure (and view of the UMLS).
INSTALL
To install these modules run:
perl Makefile.PL
make
make test
make install
This will install the modules in the standard locations. You will, most
probably, require root privileges to install in standard system
directories. To install in a non-standard directory, specify a prefix
during the 'perl Makefile.PL' stage as:
perl Makefile.PL PREFIX=/home
It is possible to modify other parameters during installation. The
details of these can be found in the ExtUtils::MakeMaker
documentation. However, it is highly recommended not messing around
with other parameters, unless you know what you're doing.
SOFTWARE COPYRIGHT AND LICENSE
Copyright (C) 2004-2009 Bridget T McInnes, Siddharth Patwardhan,
Serguei Pakhomov and Ted Pedersen
This suite of programs is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as published
by the Free Software Foundation; either version 2 of the License, or (at
your option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
Note: The text of the GNU General Public License is provided in the file
'GPL.txt' that you should have received with this distribution.
REFERENCES
1 Wu Z. and Palmer M. 1994. Verb Semantics and Lexical Selection. In
Proceedings of the 32nd Annual Meeting of the Association for
Computational Linguistics. Las Cruces, New Mexico.
2 Resnik P. 1995. Using information content to evaluate semantic
similarity. In Proceedings of the 14th International Joint
Conference on Artificial Intelligence, pages 448-453, Montreal.
3 Jiang J. and Conrath D. 1997. Semantic similarity based on corpus
statistics and lexical taxonomy. In Proceedings of International
Conference on Research in Computational Linguistics, Taiwan.
4 Fellbaum C., editor. WordNet: An electronic lexical database. MIT
Press, 1998.
5 Leacock C. and Chodorow M. 1998. Combining local context and WordNet
similarity for word sense identification. In Fellbaum 1998, pp.
265-283.
6 Lin D. 1998. An information-theoretic definition of similarity. In
Proceedings of the 15th International Conference on Machine
Learning, Madison, WI.
7 Hirst G. and St-Onge D. 1998. Lexical Chains as representations of
context for the detection and correction of malapropisms. In
Fellbaum 1998, pp. 305-332.
8 Schütze H. 1998. Automatic Word Sense Discrimination. Computational
Linguistics, 24(1):97-123.
9 Resnik P. 1999. Semantic Similarity in a Taxonomy: An Information-
Based Measure and its Applications to Problems of Ambiguity in
Natural Language. Journal of Artificial Intelligence Research, 11,
95-130.
10 Budanitsky A. and Hirst G. 2001. Semantic distance in WordNet: An
experimental, application-oriented evaluation of five measures. In
Workshop on WordNet and Other Lexical Resources, Second meeting of
the North American Chapter of the Association for Computational
Linguistics. Pittsburgh, PA.
11 Banerjee S. and Pedersen T. 2002. An Adapted Lesk Algorithm for Word
Sense Disambiguation Using WordNet. In Proceeding of the Fourth
International Conference on Computational Linguistics and
Intelligent Text Processing (CICLING-02). Mexico City.
12 Patwardhan S., Banerjee S. and Pedersen T. 2002. Using Semantic
Relatedness for Word Sense Disambiguation. In Proceedings of the
Fourth International Conference on Intelligent Text Processing and
Computational Linguistics, Mexico City.
13 Banerjee S. Adapting the Lesk algorithm for word sense
disambiguation to WordNet. Master Thesis, University of Minnesota,
Duluth, 2002.
14 Patwardhan S. Incorporating dictionary and corpus information into a
vector measure of semantic relatedness. Master Thesis, University of
Minnesota, Duluth, 2003.
15 Rada, R., Mili, H., Bicknell, E. and Blettner, M. Development and
application of a metric on semantic nets. In Proceedings of the
IEEE Transactions on Systems, Man, and Cybernetics, volume 19,
pages 17-30, 1989.
16 Nguyen, H.A. and Al-Mubaid, H. New ontology based semantic
similarity mesaure for the biomedical domain. In Proceedings of
the IEEE International Conference on Granular Computing, pages
623-628, 2006.
SEE ALSO
CONTACT US
If you have any trouble installing and using UMLS-Interface, please
contact us via the users mailing list :
umls-similarity@yahoogroups.com
You can join this group by going to:
You may also contact us directly if you prefer :
Bridget T. McInnes: bthomson at cs.umn.edu
Ted Pedersen : tpederse at d.umn.edu
AUTHORS
Bridget T McInnes, University of Minnesota Twin Cities
bthomson at cs.umn.edu
Siddharth Patwardhan, University of Utah
sidd at cs.utah.edu
Serguei Pakhomov, University of Minnesota Twin Cities
pakh002 at umn.edu
Ted Pedersen, University of Minnesota Duluth
tpederse at d.umn.edu
DOCUMENTATION COPYRIGHT AND LICENSE
Copyright (C) 2003-2009 Bridget T. McInnes, Siddharth Patwardhan,
Serguei Pakhomov and Ted Pedersen.
Permission is granted to copy, distribute and/or modify this document
under the terms of the GNU Free Documentation License, Version 1.2 or
any later version published by the Free Software Foundation; with no
Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
Note: a copy of the GNU Free Documentation License is available on the
web at:
and is included in this distribution as FDL.txt.